System and method for generating optimal bill/payment schedule

ABSTRACT

A method and system for generating bill payment schedule utilizes a composite pricing module to generate payment schedule over a predetermined period of time. In one aspect, a fraction of each pricing model attributing to the composite pricing model is determined. A charge fee associated with said each pricing model based on said fraction and said total price to charge is determined. Price to charge during each time unit of the time period is allocated, based on budget over the time period, discount rate, target profit margin and risk affordance. Bill schedule is generated using the allocated price.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following commonly-owned, co-pendingUnited States patent applications filed on even date herewith, theentire contents and disclosure of each of which is expresslyincorporated by reference herein as if fully set forth herein. U.S.patent application Ser. No. 12/040,579, for “SYSTEM AND METHOD FORCOMPOSITE PRICING OF SERVICES TO PROVIDE OPTIMAL BILL SCHEDULE”; U.S.patent application Ser. No. 12/040,481, for “SYSTEM AND METHOD FORCALCULATING POTENTIAL MAXIMAL PRICE AND SHARE RATE”; U.S. patentapplication Ser. No. 12/040,472, for “SYSTEM AND METHOD FOR CALCULATINGPIECEWISE PRICE AND INCENTIVE”.

FIELD OF THE INVENTION

The present application generally relates to pricing of services, andmore particularly to generating optimal bill and/or payment schedule.

BACKGROUND OF THE INVENTION

Buyers and suppliers of information technology (IT) services today workwith a variety of different pricing schemes to meet their individualproject and business needs. Historically, the great majority of servicecontracts were billed on a time and materials basis. However, a recentmarket and business survey revealed that users and vendors areincreasingly moving toward more flexible contract structures builtaround a combination of fixed-fee/fixed-bid service components andvalue-based/risk-reward mechanisms based on usage or definedservice-level objectives.

Common approaches to pricing include cost-oriented pricing,competitive-oriented pricing, and value-based pricing approaches. Incost-oriented pricing, the seller determines the cost involved inproviding a specific service and adds the desired profit margin tocalculate price. The cost is set based on the internal cost to deliverthe service and/or product plus a target margin on the cost. Incompetitive-oriented pricing, price is determined with reference to theprices of the competitors.

Value based pricing usually refers to the setting of price as a functionof the expected value to be derived from the services and/or products. Aset of value drivers in value-based pricing may vary from industry toindustry. In a value based approach the price is based on the totalvalue delivered to the client. Internal costs and target margins areonly considered to ensure that the value-based price meets or exceedsthe planned target margin. Value based pricing can provide greaternegotiating leverage and ability to win the contract for services and/orproducts, and typically results in the higher profit margins. Thus, moreand more projects are using value-based pricing model.

Different value-based pricing models focus on different aspects forproviding value-based pricing. For instance, part fixed/part risk-rewardpricing model is a form of value-based pricing models that links theprice to clearly defined business value improvements, for example,economic value to the customer for the goods/services that is provided.This economic value can be measured in additional revenue, cost savings,improved cash flow, inventory turns, etc. The following formulasillustrate some examples of determining value-based price using economicvalues:

-   -   Base Fee+gain sharing on cost savings (e.g., −10% cost savings        every year for 3 years);    -   Base Fee+gain sharing on completion date (e.g., +/−10% depending        on defined implementation date);    -   Base Fee+gain sharing on added value (e.g., link price to        efficiency business process improvement);    -   Base Fee+gain sharing on company level metrics (e.g., link price        to corporate level metrics such as ROCE (Return on Capital        Employed), ROA (Return on Assets); share price improvement of        the client; KPIs (Key Performance Indicators) specified in        balanced scorecard, meeting schedule, budget, and/or quality in        project delivery; building capability in process and/or        technology platform; client satisfaction).

Another example of value-based pricing model is self-funding pricingmodel. This model considers risks based on phased funding uponattainment of benefits. For example, first phase of work is funded basedon the successful attainment of benefit for the next phases of work.Solution financing model provides yet another variation of value-basedpricing model that includes complete or partial financing of anappropriate solution. Completely variable pricing is another value-basedpricing model and links the price to clearly defined business valueimprovements and covers the entire project fee plus potential gainsharing based on some metrics. Utility/on-demand pricing is yet anotherexample of value-based pricing model, in the form of “usage-based” feed,that is, price depending on usage of services, outsourced processperformance, IT infrastructure usage.

While many IT services firms utilize the value-based pricing models,others have varied pricing determination depending on the state ofclient's business goals and individual projects. For instance, ifclient's underlying business goals and maturity of its internalprocesses are small and have poorly scoped engagements, time andmaterials pricing is seen as the appropriate pricing model. On the otherhand, if the client has well defined projects drawn from previousproject experience, fixed-fee pricing is viewed as more appropriate.Among trusted partners, where the responsibilities of each player areclear and agreeable, value-based pricing is preferred since outstandingresults can be delivered if done properly.

In practice, deals may incorporate a variety of components andsituations resulting in a hybrid deal structure. Thus, it is desirableto have an automated system and method that can take into account thevarious and hybrid characteristics of a project or business goal andprovide an optimal pricing model, for example, that is based ondifferent pricing models for different sets of characteristics found inthe overall project or business goal.

Profitability can be extremely sensitive to changes in price. Forinstance, studies show that given a cost structure typical of largecorporations, a 1% boost in price realization yields a net income gainof 12%. A pricing: model that considers hybrid characteristics of aproject and uses different pricing schemes and further optimizes theratio of the usage of those different pricing schemes in the pricingmodel would provide better and more accurate pricing, and result in muchimproved profit.

BRIEF SUMMARY OF THE INVENTION

A method for generating bill payment schedule in one aspect may comprisedetermining a fraction of each pricing model attributing to a compositepricing model and determining target profit margin and risk affordance.The method may further include determining total price to charge andcomputing a charge fee associated with said each pricing model based onsaid fraction and said total price to charge. The method may alsoinclude determining time period for payment, budget and discount rate,allocating price to charge during each time unit of the time periodbased on said charge fee associated with said each pricing model, saidbudget and said discount rate, and generating a bill schedule based onsaid price to during each time unit.

A method for generating bill payment schedule in another aspect maycomprise establishing one or more elementary pricing models and one ormore pricing parameters, and constructing a composite pricing modelbased on said one or more elementary pricing models and one or morepricing parameters. The method may further include optimizing thecomposite pricing model to minimize risk and maximize one or moreselected criteria, and generating a bill schedule utilizing theoptimized composite pricing model.

A system for generating bill payment schedule may comprise a compositepricing model optimized and generated based on a plurality of pricingmodels and selected parameters. The composite pricing model is operableto determine total price to charge. The system may also include meansfor determining a fraction of each pricing model attributing to thecomposite pricing model, means for determining target profit margin andrisk affordance. The system may further includes means for computing acharge fee associated with said each pricing model based on saidfraction and said total price to charge, and means for determining timeperiod for payment, budget and discount rate. The system may alsoinclude means for allocating price to charge during each time unit ofthe time period based on said charge fee associated with said eachpricing model, said budget and said discount rate, and means forgenerating a bill schedule based on said price to during each time unit.The means of the system may be computer processor, hardware, software,firmware, or like.

A program storage device readable by a machine, tangibly embodying aprogram of instructions executable by the machine to perform a method ofgenerating bill payment schedule may be also provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an architectural diagram illustrating components of compositepricing of services in one embodiment of the present disclosure.

FIG. 2 is a flow diagram illustrating composite pricing of services inone embodiment of the present disclosure.

FIG. 3 is a diagram illustrating composite pricing model optimization inone embodiment of the present disclosure.

FIG. 4 illustrates an example of user interface screen shot that may beprovided for user interaction in one embodiment of the presentdisclosure.

FIG. 5 shows another example of user interface object for allowing userinteraction.

FIG. 6 shows pricing optimizer in more detail in one embodiment.

FIG. 7 illustrates a pricing analyzer in more detail in one embodiment.

FIG. 8 illustrates a what-if-scenario-analysis example.

FIG. 9 shows a sensitivity analysis example.

FIG. 10 shows simulation analysis example.

FIG. 11 is a flow diagram illustrating a bill scheduling method in oneembodiment of the present disclosure.

FIG. 12 illustrates an example of a bill schedule in one embodiment ofthe present disclosure.

FIG. 13 shows an example of metrics and its probability distribution.

FIG. 14 shows an example of a Call Center template.

DETAILED DESCRIPTION

The system and method of the present disclosure in one embodimentprovide a pricing model, and enable analysis of multi-faceted, forexample, multi-phased, multi-business unit, multi-process,multi-geo/country deal structure or service project with its parts andphases having different pricing implications. The system and method ofthe present disclosure also provide flexible, composite pricingschedule. The schedule in one embodiment is optimized for both serviceprovider and service receiver by gain and risk sharing, and is based onboth cost and value based pricing combination.

FIG. 1 is an architectural diagram illustrating examples of systemcomponents for providing composite pricing of services in one embodimentof the present disclosure. The various modules shown in FIG. 1 arelogical or functional components illustrated as examples to explain theworkings of the system of the present disclosure in one embodiment, andmay be implemented and run on general and/or special purpose computer orcomputers, for instance, as software, firmware and/or hardware or likecomponents. A person of ordinary skill in the art will understand thatthe components need not be divided or modularized only as shown inFIG. 1. Thus, for example, the functional components may be implementedas one unit or as many different units of software, hardware, circuitryor like.

Referring to FIG. 1, a pricing model composer 102 constructs one or morecomposite pricing models using one or more elementary pricing modelsselected in pricing model selection module 104, and optimizes thepricing models based on one or more pricing parameters selected inmodule 106, which in one embodiment include metrics for both cost andvalue. Parameters refer to variables that define characteristics andbehavior of pricing scheme in each pricing model, and are used toconfigure a pricing model. Parameter values can be input to the pricingmodel. The pricing model composer 102 utilizes an asset repository 128and templates 130 to efficiently construct the composite pricing models.

In one embodiment, templates 130 of pricing models may be composed bypricing experts and/or by using historical data from previous deals.Templates of pricing models have their parameter values set as defaultvalues based on historical data and other reasoning that are configuredfor cases. Templates of various pricing models are grouped together foruse in different deal cases. The information on the cases and groups ofpricing model templates for the cases may be stored in a repository. Forinstance, the Case Repository 128 can be a database, which allows searchfor templates for deal cases. While the present disclosure does notconstrain the structure and enabling technologies for the caserepository and templates, they can be network-based systems usingrepresentation languages, e.g., Web-based repository storing templatesrepresented in XML or HTML, etc. Case Repository 128 may be alsoreferred to as Asset Repository. Generally, administrators may managethe repository 128. A user may update the data in the repository, forinstance, add new templates, remove templates and/or update templates.

Templates 130 may include one or more pricing models, for example,elementary pricing models, and default values for the parametersassociated with the pricing models. As an example, a template may be acomposition of several elementary pricing models. Generally, differentsolutions have corresponding templates. For instance, call centersolution has a corresponding call center template, outsourcing solutionhas a corresponding outsourcing solution template, customer relationshipmanagement solution has a corresponding customer relationship managementtemplate, etc. An example of a Call Center template is shown in FIG. 14.This template shown in FIG. 14 has composite of three pricing models,fixed price, performance adjusted, and fully business metric aligned.The template also shows parameters and values associated with thosethree pricing models.

Examples of elementary pricing models include but are not limited to,time and materials based, fixed fee, payment phasing/smoothing,performance adjusted, share of benefits adjusted, utility-based fee,fully business metric aligned, etc. Briefly, time and materialselementary service pricing model is tied to resource usage and mayinclude “not to exceed” conditions. Examples of such conditions mayinclude but are not limited to, “the FTE level in 2008 not to exceed100,” “total annual FTE level not to exceed 200,” “total consulting costnot to exceed 200% of the total software and hardware cost combined.”FTE refers to Full Time Equivalent, a unit for measuring work effort inservice projects or a generic unit for Head Count. This pricing modelmay be suitable for situations, in which the work effort is unknown,business case is unknown or not knowable or not discoverable, or thescope of deal structure is unclear or highly subject to change orvolatility. Parameters such as FTE, skill based rates, software andhardware involved, may be used in the time and materials elementaryservice pricing model.

Fixed price based elementary pricing model is tied to a specificdeliverable or deliverables, the scope of the deal structure that doesnot vary according to work effort or other factors, or combinationsthereof. Fixed price based elementary pricing model may be appropriatefor cases in which work effort can be fairly accurately estimated, thescope of the work or project is clearly defined, and/or futureexpenditures are predictable. A parameter such as profit margin is usedin this pricing model. For example, a desired level of the parametersmay be given as input; the pricing model may output the expected levelof the parameters for the selected scheme after optimization.

Payment phasing/smoothing pricing model works with payment installments,projects divided into multiple phases in which subsequent project phasesmay depend on the degree of success of the previous phases. This modelmay be fitting for cases in which there is a promise of early returns,client funding is not immediately available, and/or imminent kickoff isdesired or required. A parameter such as phase funding is used in thispricing model. Desired level of the parameters may be provided as input;the pricing model outputs the expected level of the parameters for theselected scheme after optimization.

Performance adjusted pricing model places a percentage of base fees atrisk and links the remainder to clearly defined deliverables, milestonesor service level agreements. This model may be suitable for cases inwhich work effort can be fairly accurately estimated, the scope of workand tasks is clearly defined, and/or client is seeking to mitigatedelivery risks. Parameters such as deliverables, milestones, servicelevel agreements, quality measures, project duration, budget, clientsatisfaction, capability building may be used in this pricing model.Desired level of the parameters may be provided as input; the pricingmodel outputs the expected level of the parameters for the selectedscheme after optimization.

Share of benefits adjusted pricing model places a percentage of basefees at risk and links the remainder to clearly defined business valueimprovements. This model may be befitting for cases in which there is aclear point-of-view on business benefits and/or future expenditures aresomewhat predictable. Parameters such as percentage of client's netrevenue, cost savings, pre-tax income (PTI), gross profit (GP), paybackperiod, internal return rate (IRR) may be used in this pricing model.Desired level of the parameters may be provided as input; the pricingmodel outputs the expected level of the parameters for the selectedscheme after optimization.

Utility based pricing model describes pricing in the form of usage-basedfee, depending on usage of services, outsourced process performance, ITinfrastructure usage, etc. Utility based pricing model may beappropriate for cases in which the historical or comparative volumes areknown, and/or future volumes are unpredictable or highly variable.Parameters or factors such as volume of transaction, size of centralprocessing unit (CPU), usage of service, outsourced process performance,IT infrastructure usage, time-based licensing (TBL), perpetual licensingfactors may be used in this pricing model. Desired level of theparameters may be provided as input; the pricing model outputs theexpected level of the parameters for the selected scheme afteroptimization.

Elementary service pricing model that is fully business metric alignedis linked to clearly defined business value improvements and covers theentire project fee and gain sharing based on an agreed upon businessmetrics. This model may be suited to cases in which there is a clearpoint-of-view on business benefits, output can be directly linked tobusiness metrics, and/or business metrics are tracked. Parameters orfactors such as business growth, service level agreements, cycle time,return on capital employed (ROCE), return on assets (ROA), businessprocess improvement, service request duration, entitlement drivenincident avoidance, remote solve rate, Web self help effectiveness,freight cost, total parts usage cost technical fault rate, no faultincident rate may be used in this pricing model. Desired level of theparameters may be provided as input; the pricing model outputs theexpected level of the parameters for the selected scheme afteroptimization.

Referring to FIG. 1, pricing model selection module 104 may select oneor more elementary pricing models based on one or more factors orcriteria associated with the deal structure such as budget 110, baseline112, project type 114 and desired solution or benefit 116. Parameterselection module 106 selects various pricing parameters using analyticsto understand the uncertainty inherent in cost estimates 118 andexpected value 120. For example, consider a scenario in which thepricing model selection module 104 selected a time and material model asone of the elementary models for pricing composition. The metricsselection module 106 calculates or determines the uncertainty ofmetrics, i.e., parameters, of the time and material-based pricing, i.e.,the uncertainty in the cost estimation of the pricing model. The metricsselection module 106 may determine the uncertainty based on historicaldata or user input. The metrics selection module 106 identifies all theparameters of the model—FTE, work scope, staffing, rates, etc. As anexample, the metrics selection module 106 may identify the parameters byidentifying user's selection of pricing model and retrieving parametersassociated with that pricing model from parameter database. Another wayto identify the parameters is to use the template selected and importedfor the solution. A template contains pricing model identifications andassociated parameters. If the module 104 also selected anotherelementary model, e.g., fixed pricing for a phase of the project, themetrics selection module 106 identifies parameters for this model, butdoes not ask the user to provide the same parameters value again, sincethe parameters are common to both models. Generally, pricing models haveassociated parameters. The system and method of the present disclosuremay collect and store many parameters and link them to respectivepricing models. When a user or the system selects the elementary pricingmodels, for instance, in 104, the corresponding parameters associatedwith the selected pricing model or models may be listed for a user toselect and input values.

In one embodiment, pricing model selection module 104 may automaticallyevaluate the client situation and the requirements for success to selectthe most appropriate deal type. In another embodiment, a user maymanually select and provide the selected model. Example of the factorsconsidered in selecting the pricing model is shown in Table 1. Table 1illustrates an overview of each deal type, which for example, may beused as a reference guide during the evaluation process. For automaticselection process, a series of if-then rules or like can be implementedto automatically determine the appropriate pricing model based on thespecific client situation, description and requirements for success.

TABLE 1 Requirements for Pricing Models Client Situation DescriptionSuccess Time and Materials Scope unclear Pricing tied to Tight projectresource usage management from the client Fixed Price Wants deliveryPricing tied to Accurate work guarantee deliverables or effort estimatescope Payment Phasing No immediate Timing of payments Client pays riskand Smoothing funding linked to benefits premium Performance Mitigatedelivery Some fees linked to Client pays for Adjusted risk deliveryfinancing performance Share of Profits Wants “skin in the Some feeslinked to Business case can Adjusted game” business value affordfinancing change Utility Wants variable costs Pricing linked to Scope isclearly usage of services defined Fully Business Completebenefits-focused Pricing linked Client hands over Metric alignedentirely to benefits some control

Pricing optimizer 122 computes optimal bill schedule, for example, byusing the defined composite pricing model and considering one or moreconstraints. One or more constraints may include but are not limited to,budget and profit margin. Pricing optimizer 110 may maximize one or moredesired factors or criteria, for example, benefits and/or rewards suchas client benefit, customer satisfaction and provider profit, whileminimizing one or more risks, etc, and takes a portfolio approach topricing optimization and risk management. Examples of risks minimizedmay include but are not limited to, risks associated with IT (systemfailure, malfunction, etc.), security (security failure, hacker attacks,etc.), finance (cash flow problems), resources (problems in workforcedemand and supply), global workforce (communication problem, culturebarrier, etc.), third party participation (legal issues in contract,etc.).

Pricing analyzer 124 enables business case analyses for various what-ifscenarios for understanding potential benefit and risk of alternativepricing options, and/or sensitivity analyses for understanding theimpact of parameter value changes on overall result, for instance, fornegotiation support. For example, negotiations to reach an agreement ondeal structure between client and provider can utilize different pricingstructures produced by varying the parameters. Pricing analyzer 124 alsomay evaluate the risk of changing all the variables at the same timewhile introducing probability distributions for each variable, forinstance, utilizing simulation methods such as Monte Carlo simulation.Thus, pricing analyzer 124 can provide the impact of individualparameters on the overall result, which can then be used for negotiationsupport, for instance, for reaching an agreement with a pricing proposal126.

FIG. 2 is a flow diagram illustrating composite pricing of services inone embodiment of the present disclosure. At 202, a user may inputsolution information and select one or more templates. Alternatively oradditionally, the information may be automatically selected andretrieved, for example, from a repository, database or like, or anothercomputer system or like, storing or in possession of such information.Generally, the solution may indicate the service the contract wouldrequire the service provider to deliver. For example, the information onthe solution here may include what is needed to calculate the cost andvalue of the solution, which may include the work scope, work effort,client and project information the industry and sector of the client,the continent and countries included in the scope, desired projectstructure, timeline, etc. Templates as explained above may includeelementary pricing models with their parameters set based on selectedreasoning to make the deal attractive.

At 204, a user may be prompted with questions to which the user inputsanswers with information on solution. This information may also beobtained automatically from a source such as a repository, database orlike, or another computer system or like storing or in possession ofsuch information. At 205, a user may select appropriate pricing model,for instance, by considering various criteria such as those shown inTable 1, and parameters. Alternatively, or additionally at 208, one ormore appropriate pricing models and parameters may be automaticallyselected and suggested. At 210, a composite pricing model is suggested,which may comprise one or more selected pricing models. At 212, theprice is generated using the composite pricing model. For example, thesystem and method of the present disclosure uses a pricing optimizer andgenerates a bill schedule. At 214, analysis capability for the desiredor requested solution is provided. After the composite pricing model isdetermined for a solution, a user may analyze the pricing model.

Examples of forms of analysis may include, but are not limited to, whatif scenario analysis, sensitivity analysis, and Monte Carlo simulation.Sensitivity analysis is useful in understanding the variables' influenceon final output, i.e., the impact of parameter value changes on overallresult. What-if scenarios can provide an understanding of potentialbenefit and risk of alternative pricing options. Such information may beused, for instance, for negotiation support. Monte Carlo simulation maybe utilized to evaluate the risk of changing all the variables at thesame time while introducing probability distributions for each metrics.Thus, for instance, at 218, a pricing analyzer (FIG. 1, 124) mayarticulate potential benefit and risk of different pricing options,including best-case, worst-case and most likely scenarios. Each metrichas probability distribution. For example, triangle distribution hasminimum, most likely, and maximum values. An example of metrics and itsprobability distribution is shown in FIG. 13. The metrics (parameters)and their value 1302 may be obtained from the information shown in FIG.4, for instance, from user input values. Referring to FIG. 13, thetriangle distribution is used as an example. Based on the minimum value,most likely value and maximum value of a metric, the triangledistribution function 1304 and random number generation formulation 1306can be generated. Random Number Generation 1306 generates a plurality ofstatic random numbers for triangle distributions, for example run n=1000samples of the composite pricing model. Reference item 1308 shows thesimulation result with 1000 samples, which can be used as input forsimulation analysis, for instance, shown in FIG. 10. The net presentvalue of the bill schedule generated using the pricing optimizer, mayinclude the best case, worst case and most likely case. Referring backto FIG. 2, as shown at 220, sensitivity analysis also may be executed tounderstand the influence of individual parameters on the overall result,for instance, for negotiation support. At 216, various reports may begenerated.

FIG. 3 is a diagram illustrating composite pricing model optimization inone embodiment of the present disclosure. A user or an automated processselects one or more appropriate models 304 that fit the characteristicsof a project or business goal from a set or list of pricing models 302.In the example shown in FIG. 3, share of benefits, utility, performanceadjusted, and time and materials pricing models are selected from agroup of different pricing models shown at 302 to form a compositepricing model 304 for a given project. Optimization algorithm or like isused to minimize the risks associated with using the selected pricingmodels 304 for the given project. The result of this optimization at 306tells the proportion of the selected models that should be utilized inthe composite pricing model in order to minimize the risks or other likecriteria involved with the pricing of the overall project. Thisrisk-minimized composite pricing model 306 is again optimized, tomaximize the benefits or rewards or other like criteria, for example,customer satisfaction. A composite pricing model shown at 308 results,optimized for minimum risk and maximum benefit. The entire process ofselecting appropriate models 310, minimizing risk portfolio 312, andmaximizing benefits or rewards 314 may be performed automatically orsubstantially automatically using a computer system or processor, forinstance. A user interface software or system may be provided to aid theuser in interacting with the system, for instance, for inputtingvariables such as selected models, parameters, and also for presentingreports and analyses.

FIG. 4 illustrates an example of user interface screen shot that may beprovided for user interaction. A user may, for example, choose a pricingmodel by selecting a solution provided by the pricing model. Forinstance, a user may specify a pricing model and parameters as shown at402. A user may also select parameter value type and values such asminimum, mostly likely and maximum values. The input values may be usedfor pricing analysis as described with reference to FIG. 13. In anotherembodiment, rather than directly selecting a pricing model, a user mayselect a desired solution. The system may then automatically list one ormore pricing models that provide that solution. The pricing model ismaintainable and can be described with more detailed parameters as shownin FIG. 4. Any other method or means for choosing appropriate pricingmodels and parameters for a given project or deal type may be used.

FIG. 5 shows another example of a user interface object that enablesuser interaction. With this object 500, a user may choose the parametersfor use in the pricing model for determining the price. For example,shown at 502, the performance adjusted pricing model, project durationparameter, mean value of 10 and standard deviation value were chosen bya user. An internal algorithm, for example, may estimate the significantparameters automatically. Significance estimation is a process thatscreens the parameters and evaluates whether the parameter issignificant. For example, a parameter such as the project duration whosedeviation value is large (e.g., 1 month˜100 months) may indicate thatthe project duration is highly uncertain. Selecting and using suchparameters may be risky for the pricing model. Therefore, an automaticalgorithm may recommend against using such parameters. A user mayfurther revise the options based on the significance estimation. Thenumber of parameters that can be used is not constrained in oneembodiment.

The following equation is an example of the optimization model fordetermining significance estimation.

${Max}\frac{\sum\limits_{i = 1}^{n}{x_{i} \times {Mean}\mspace{11mu}( {Parameter}_{i} )}}{\sqrt{\sum\limits_{i = 1}^{n}{x_{i} \times {SD}\mspace{11mu}( {Parameter}_{i} )^{2}}}}$s.t.  x₁, x₂, …  x_(n) ∈ {0, 1}

Mean( ) is a function to calculate the mean of parameter i, SD( ) is afunction to calculate the standard deviation of parameter i, x_(i) is akind of Boolean variable to represent whether the parameter i issignificant or not.

As an example, a pricing model of the present disclosure in oneembodiment may assume the following for simplicity and for explanationsake:

-   -   1. There are four types of fee in the model, including fixed        fee, performance adjusted, utility, and share of benefit        adjusted. In fact, the research framework is similar if there        are more types for the fee that can be grouped into fixed fee        and variable fee.    -   2. The variable fee is paid in one period after the solution is        implemented to avoid the problem of compound interest.    -   3. There are no explicit correlations among the variable fees.        Actually, more usage leads to more benefit and better        performance.    -   4. For each variable fee, its formulation is a proportional        function. For example, the usage fee function is: u=k*usage,        where k represents some proportional value. In this example        variable fee model, the fee is a function of usage, and the        formula states that the fee increases linearly to the increase        of usage. In this linear function k represents a constant, which        decides the degree of the increase (or decrease). This linear        function is an embodiment of the variable fee model. There may        be various different models, including non-linear functions.

In one embodiment of the present disclosure, a two-stage pricing modelis introduced to solve a pricing problem reversely, to decide theproportion among the variable fee first and then the proportion of fixedfee as well as the other variable fees. At the first stage in oneembodiment, the proportion of the performance fee, benefit fee and usagefee is solved to minimize the total risk of the variable fee, since therisk of the variable fee should be minimized if the minimization oftotal risk of the charge is expected. At the second stage in oneembodiment, based on the customers' satisfactory and the providers' riskaffordance, the proportion of the fixed fee and the variable fee can beinferred. Combined with the solution of the variable fees in the firststage, all the proportion of the four charges are obtained.

Stage 1. Assume the proportion of the performance fee, benefit fee andusage fee is p, b, u respectively. The data of the performance, benefitand usage can be gathered from the history or users' experience. Assumethe mean and standard deviation of the data is m_(p), m_(b), m_(u) andσ_(p), σ_(b), σ_(u). Then the risk that performance fee brings to thewhole charge is

${( \frac{p}{m_{p}} )^{2}\sigma_{p}^{2}},$so the total risk of the variable fee is the sum of the three fee'srisk. The problem can be written as:

$\begin{matrix}{{{{\min( \frac{p}{m_{p}} )}^{2}\sigma_{p}^{2}} + {( \frac{b}{m_{b}} )^{2}\sigma_{b}^{2}} + {( \frac{u}{m_{u}} )^{2}\sigma_{u}^{2}}}{{{s.t.\mspace{14mu} p} + b + u} = 1}} & (1)\end{matrix}$Using Lagrange algorithm, equation 1 can be solved:

$\begin{matrix}{{\frac{\partial L}{\partial p} = {{{2{p( \frac{\sigma_{p}}{m_{p}} )}^{2}} - \lambda} = 0}}{\frac{\partial L}{\partial b} = {{{2{b( \frac{\sigma_{b}}{m_{b}} )}^{2}} - \lambda} = 0}}{\frac{\partial L}{\partial u} = {{{2{u( \frac{\sigma_{u}}{m_{u}} )}^{2}} - \lambda} = 0}}} & (2)\end{matrix}$Then the proportions of the three variable fees are:

$\begin{matrix}{{p_{0} = {{\frac{BC}{{AC} + {BC} + {AB}}\mspace{160mu} A} = ( \frac{\sigma_{p}}{m_{p}} )^{2}}}{b_{0} = {{\frac{A\; C}{{AC} + {BC} + {AB}}\mspace{50mu}{where}\mspace{50mu} B} = ( \frac{\sigma_{b}}{m_{b}} )^{2}}}{u_{0} = {{\frac{AB}{{AC} + {BC} + {AB}}\mspace{160mu} C} = ( \frac{\sigma_{u}}{m_{u}} )^{2}}}} & (3)\end{matrix}$Then the standard deviation of the variable fee is:

$\begin{matrix}{\sigma = \sqrt{{( \frac{p_{0}}{m_{p}} )^{2}\sigma_{p}^{2}} + {( \frac{b_{0}}{m_{b}} )^{2}\sigma_{b}^{2}} + {( \frac{u_{0}}{m_{u}} )^{2}\sigma_{u}^{2}}}} & (4)\end{matrix}$

Stage 2. For a specific solution project, the customer has his ownjudgment of the value, and he is clear about his satisfactory level whencharging different amount with different fees. So we can get these databy interviewing them, from which we can calculate the elasticity ofsatisfactory to the charged fee. Assume the customers' satisfactory is Sand the relative fee is F₀ when charging all by fixed fee, theelasticity of satisfactory is E_(p), E_(b), E_(u), the risk affordanceof the providers is R, which can be described as the money amount thatthe provider is willing to lose at 5% possibility level.

From the satisfactory view, one dollar increase of fixed fee will equal

$\frac{E_{f}}{E_{p}},\frac{E_{f}}{E_{b}},\frac{E_{f}}{E_{u}}$dollar increase of performance fee, benefit fee and usage fee. Then onedollar of fixed fee equals to

$r = {{p_{0}\frac{E_{f}}{E_{p}}} + {b_{0}\frac{E_{f}}{E_{b}}} + {u_{0}\frac{E_{f}}{E_{u}}}}$increase of the whole variable fee based on Stage 1. Assume that theproportion of fixed fee is f, then the total fee can be charged atcertain satisfactory level is F₀f+rF₀(1−f).

Then next focus is on the solution provider. The risk of the provider isrF₀(1−f)σ, which should be less than the provider's risk affordance. Asthe customer prefers more variable fee, then the optimal solution shouldbe maximize the variable fee rF₀(1−f). Therefore, the variable feeshould be

$\frac{R}{\sigma},$and the proportion of the fixed fee can be consequently deducted.

Combined with the solution of Stage 1, it is concluded that:

${F = {F_{0} - \frac{R}{r\;\sigma}}};{P = {\frac{R}{\sigma}p_{0}}}$${B = {\frac{R}{\sigma}b_{0}}};{U = {\frac{R}{\sigma}u_{0}}}$

Customer satisfaction analysis may utilize Analytical Hierarchy Process(AHP) developed by Thomas Saaty. AHP provides a proven, effective meansto deal with complex decision making and can assist with identifying andweighting selection criteria, analyzing the data collected for thecriteria and expediting the decision-making process. In the presentdisclosure in one embodiment, AHP method may be utilized to discovercustomer's preference on pricing models.

The pricing optimization algorithm described above is shown as anexample, and the method and system of the present disclosure is notlimited to using that model only. Rather, a person of ordinary skillwill appreciate that other optimization models may be formulated andused.

FIG. 6 shows a pricing optimizer in more detail in one embodiment. Apricing optimizer 602 (also shown as 122 in FIG. 1) receives as input604 one or more selected pricing models (e.g., elementary pricingmodels), corresponding parameters, and value distribution of eachparameter. The optimizer 602 minimizes risk portfolio at 606. Usingadditional input 608, the optimizer 602 further maximizes customersatisfaction at 610. Additional input 608 may include customer'spreference for each pricing model. Methods such as Analytic HierarchyProcess may be used to obtain customer preferences. The optimizer 602then may generate bill scheduling 616 (also referred to as price orpricing) at 614 using input values 612 such as the number of years tocharge, target profit margin, start date, end date and allocation ratefor each pricing model, risk affordance, and discount rate. Those inputvalues are listed herein as examples. A person of ordinary skill willunderstand that one or different combinations of the listed parameters,or additional parameters may be used to create a bill payment scheduleat 616. An example of the bill payment schedule 618 is shown in detailin FIG. 12.

FIG. 11 is a flow diagram illustrating a method of generating billschedule in one embodiment of the present disclosure. At 1102, thepercentage of each pricing model used in or attributing to a compositepricing model is retrieved. This percentage, for example, is obtainedfrom a pricing optimizer shown at 122 in FIG. 1, which optimized themodel composed in the pricing model composer also shown at 102 inFIG. 1. Any other method may be used to determine the percentage of eachpricing model from a composite model. As an example, if a compositemodel comprises fix fee, performance adjusted, and utility pricingmodels, the percentage or ratio of each pricing model that attributes tothe composite model is determined; for instance, fix fee 50%,performance adjusted 10%, utility 40% (or ratio of 5:1:4, respectively).As another example, referring to the composite model 308 shown in FIG.3, the percentages of share of benefit, usage based fee, projectperformance fee, and fixed fee would be obtained.

Referring to FIG. 11, at 1104, the target profile margin and riskaffordance values are obtained, for example, from a user as user input.Alternatively or additionally, the data may be retrieved from arepository or knowledge base. At 1106, the total price to charge isdetermined, for instance, based on the pricing optimizer's computations.At 1108, the price or amount of charge from each pricing model isdetermined. For example, if the total price is determined to be 1million USD at step 1106, the amount of charge attributed to eachpricing model is the fraction of the composite model that each pricingmodel contributes (e.g., as determined at step 1102) multiplied by 1million USD. Using the above composite model example that comprises 50%fix fee, 10% performance adjusted, and 40% utility, the amount of chargeattributed to each pricing model respectively is 500K USD, 100K USD, and400K USD.

At 1110, the number of years to charge, allocation rate, client's budgetlimit and discount rate are determined, for example, from user input,available data or knowledge base, or additional computation. Examplevalues obtained at 1110 associated with the above composite pricingmodel example are shown in Table 1. Table 1 illustrates examples ofallocation rates, in which the number of years to charge is 4, discountrate is 10%.

TABLE 1 Y1 Y2 Y3 Y4 Fix Fee 50%  30% 20% Performance 100% adjustedUtility 50% 50%

Referring to FIG. 11, at step 1112, price or charge fee is allocated toeach year, which produces bill scheduling.

The bill schedule shown as an example in FIG. 12 illustrates the amountto charge determined based on the pricing model optimizer of the presentdisclosure, during different time periods and considering factors suchas client budget over the time duration. In this example, based on theclient budget 1202 determined over four year period 1204, andconsidering discount rate of 10%, bill schedule 1206 is determinedaccording to the optimized composite pricing model. Illustratively,fixed fee 1210, project performance fee 1212, and usage based fee 1214are allocated over the determined time period that meets the clientbudget (also shown at 1208).

FIG. 7 shows a pricing analyzer in more detail in one embodiment. Apricing analyzer 702 (also shown at 124 in FIG. 1) uses bill scheduleloader 706 to load as input bill schedule 704. A bill schedule isdefined as a configuration of one or more pricing models over time,which when taken as a whole, represents a satisfactory cost or paymentfor a business solution. An example was described with reference to FIG.12. A scenario editor 708, a sensitivity analyzer 710, and a simulator712 analyze the bill schedule 704 to make one or more businessdecisions. The scenario editor 708 defines scenarios and the scenarioanalyzer compares different scenarios. The sensitivity analyzer 710 candetermine the effect on the overall bill schedule by changing onemetrics value at a time. Based on the sensitivity analysis, it is easyto identify critical metrics of pricing models and how their variabilityimpacts the result. The simulator 712 can determine the effect ofchanging all the metrics value at the same time while introducingprobability distributions for each metrics.

FIG. 8 shows a result of what-if scenario analysis, that is, billschedule of different scenarios. A scenario is a model of a hypotheticalpricing model and corresponding parameters. After a scenario is definedby, for example, the scenario editor 708 shown in FIG. 7, what ifanalysis or scenario analysis simulates the system to find possibleeffect of these scenarios, and generates report 800 shown in FIG. 8 tocompare the price over a period of time, for example, years, fordifferent scenarios.

A sensitivity analysis can be a meaningful addition to a business case,since it examines the influence of individual parameters on the overallresult. FIG. 9 shows an example of sensitivity analysis used todetermine the effect on the overall result by changing one variable at atime to understand uncertainty in any type of financial model and toidentify critical inputs of the financial model and how theirvariability impacts the result. Sensitivity analysis can describe theimpact of one metrics (cause) on the final financial results and ranksall the selected metrics. If a metric takes a different value, theanalysis captures how many percentages another metric, for instance,revenue, will be changed. The parameters and rate of change shown at 902may correspond to the user input, for instance, obtained at step 206 ofFIG. 2, and detailed information obtained, for example, via userinterface, shown in FIG. 4. An example of sensitivity analysis diagramis shown at 904. Referring to the first metric “Num. of transaction”shown in FIG. 9 as an example, when Num. of transaction 1.75 million,the revenue will be 10 million UDS. However when Num. of transaction is2.5 million (the maximal value), the revenue will increase 100%, that is20 million USD. Similarly, when Num. of transaction=1 million (theminimal value), the revenue will reduce 101%.

Due to the complexity and uncertainty in real systems, simulation isoften helpful in handling complex decisions. For example, Monte Carlosimulation can be used to determine the effect of changing all thevariables at the same time while introducing probability distributionsfor each variable. Monte Carlo simulation is known method often usedwhen the model is complex, nonlinear, or involves more than just acouple uncertain parameters. Briefly and generally, Monte Carlosimulation uses the following steps:

-   -   Step 1: Get the composite pricing model y=f(x1, x2, . . . , xq),        x1, x2, . . . , xq are the parameters;    -   Step 2: Generate a set of random inputs, xi1, xi2, . . . , xiq;    -   Step 3: Evaluate the model and store the results as yi;    -   Step 4: Repeat steps 2 and 3 for i=1 to n, n is the total number        of simples/evaluations;    -   Step 5: Analyze the results using histograms, summary        statistics, confidence intervals, etc. such as the diagram shown        at 1006 in FIG. 10.

FIG. 10 shows an example of simulation analysis. The Monte Carlosimulation generates a set of random inputs, for example, randomlygenerated parameters and their distribution. In the example shown inFIG. 10, the triangle distribution is used for random number generation.Other methods of generating random numbers to represent uncertainty maybe used. For simulation analysis, parameters of pricing model withdistribution 1002 are input. By using random inputs, the deterministicmodel is being converted into a stochastic model. Parameters and theirdistribution 1002 randomly generated are input to a composite pricingmodel 1004. An example of the composite model 1004 is the modeldetermined in the pricing optimizer shown at 122 in FIG. 1. Thestatistics of simulation result can be shown in a diagram 1006 or like.The X-axis shows the metric value. The Y-axis is frequency, whichcalculates how often values occur in the 1000 samples that are within arange of the metric values, that is, count of the number of scores thatfall within ranges of metrics value.

The method of the present disclosure in one embodiment may be embodiedas a program, software, or computer instructions embodied in a computeror machine usable or readable medium, which causes the computer ormachine to perform the steps of the method when executed on thecomputer, processor, and/or machine.

The system and method of the present disclosure may be implemented andrun on a general-purpose computer or computer system. The computersystem may be any type of known or will be known systems and maytypically include a processor, memory device, a storage device,input/output devices, internal buses, and/or a communications interfacefor communicating with other computer systems in conjunction withcommunication hardware and software, etc.

The terms “computer system” and “computer network” as may be used in thepresent application may include a variety of combinations of fixedand/or portable computer hardware, software, peripherals, and storagedevices. The computer system may include a plurality of individualcomponents that are networked or otherwise linked to performcollaboratively, or may include one or more stand-alone components. Thehardware and software components of the computer system of the presentapplication may include and may be included within fixed and portabledevices such as desktop, laptop, server. A module may be a component ofa device, software, program, or system that implements some“functionality”, which can be embodied as software, hardware, firmware,electronic circuitry, or etc.

The embodiments described above are illustrative examples and it shouldnot be construed that the present invention is limited to theseparticular embodiments. Thus, various changes and modifications may beeffected by one skilled in the art without departing from the spirit orscope of the invention as defined in the appended claims.

1. A computer-implemented method for generating bill payment schedule,comprising: determining, by a processor, a fraction of each pricingmodel attributing to a composite pricing model, the determining based onhybrid characteristics of a project wherein the composite pricing modelincludes different pricing models for different sets of characteristicsin the project and wherein the fraction can be allocated differently indifferent time units over a time period for payment, the determiningfurther based on user input of whether a value for a selected parametershould be used as historical data, triangle distribution data, or normaldistribution data and associated value for the selected parameter, thedetermining further including automatically screening, by the processor,the selected parameter for use in the pricing model and recommendingagainst using the screened parameter based on a determination made by[an automatic process using a significance estimation for determiningsignificance of the selected parameter, wherein the significanceestimation is determined using an optimization model,${Max}\frac{\sum\limits_{i = 1}^{n}{x_{i} \times {Mean}\mspace{11mu}( {Parameter}_{i} )}}{\sqrt{\sum\limits_{i = 1}^{n}{x_{i} \times {SD}\mspace{11mu}( {Parameter}_{i} )^{2}}}}$s.t.  x₁, x₂, …  x_(n) ∈ {0, 1} wherein Mean( ) is a function tocalculate the mean of parameter i, SD( ) is a function to calculate astandard deviation of parameter i, x, is a kind of Boolean variable torepresent whether the parameter i is significant or not; determiningtarget profit margin and risk affordance; determining total price tocharge; computing a charge fee associated with said each pricing modelbased on said fraction and said total price to charge; determining timeperiod for payment, budget and discount rate; allocating price to chargeduring each time unit of the time period based on said charge feeassociated with said each pricing model, said budget and said discountrate; and generating, using a processor, a bill schedule based on saidprice to charge during each time unit.
 2. The method of claim 1, whereinthe composite pricing model is generated based on selection of aplurality of pricing models.
 3. The method of claim 1, wherein thecomposite pricing model is optimized to meet one or more selectedcriteria.
 4. The method of claim 1, further including enabling analysisof said bill schedule.
 5. The method of claim 4, wherein the analysisincludes sensitivity analysis, or business case analysis, orcombinations thereof.
 6. The method of claim 4, wherein the analysisincludes determining effect on price by changing one variable at a time.7. The method of claim 4, wherein the analysis includes determiningeffect of changing all variables simultaneously while introducingprobability distributions for each of the variables.
 8. The method ofclaim 1, further including: analyzing one or more attributes associatedwith the bill schedule.
 9. The method of claim 1, further including:providing a user interface for receiving input data from a user andpresenting the bill schedules.
 10. The method of claim 1, wherein saidbill schedule includes bill schedule for payment of services, billschedule for payment of goods, or combination thereof.
 11. The method ofclaim 1, wherein the composite pricing model is generated and optimizedautomatically based on automatically selected plurality of pricingmodels.
 12. The method of claim 1, wherein the composite pricing modelis generated and optimized automatically based on user selectedplurality of pricing models.
 13. A program storage device readable by amachine, tangibly embodying a program of instructions executable by themachine to perform a method of generating bill payment schedule,comprising: determining a fraction of each pricing model attributing toa composite pricing model, the determining based on hybridcharacteristics of a project wherein the composite pricing modelincludes different pricing models for different sets of characteristicsin the project and wherein the fraction can be allocated differently indifferent time units over a time period for payment, the determiningfurther based on user input of whether a value for a selected parametershould be used as historical data, triangle distribution data, or normaldistribution data and associated value for the selected parameter, thedetermining further including automatically screening, by a processor,the selected parameter for use in a pricing model and recommendingagainst using the screened parameter based on a determination made by anautomatic process using a significance estimation for determiningsignificance of the selected parameter, wherein the significanceestimation is determined using an optimization model,${Max}\frac{\sum\limits_{i = 1}^{n}{x_{i} \times {Mean}\mspace{11mu}( {Parameter}_{i} )}}{\sqrt{\sum\limits_{i = 1}^{n}{x_{i} \times {SD}\mspace{11mu}( {Parameter}_{i} )^{2}}}}$s.t.  x₁, x₂, …  x_(n) ∈ {0, 1} wherein Mean( ) is a function tocalculate the mean of parameter i, SD( ) is a function to calculate astandard deviation of parameter i, x, is a kind of Boolean variable torepresent whether the parameter i is significant or not; determiningtarget profit margin and risk affordance; determining total price tocharge; computing a charge fee associated with said each pricing modelbased on said fraction and said total price to charge; determining timeperiod for payment, budget and discount rate; allocating price to chargeduring each time unit of the time period based on said charge feeassociated with said each pricing model, said budget and said discountrate; and generating a bill schedule based on said price to chargeduring each time unit.
 14. The program storage device of claim 13,wherein the composite pricing model is generated based on selection of aplurality of pricing models.
 15. The program storage device of claim 13,wherein the composite pricing model is optimized to meet one or moreselected criteria.
 16. The program storage device of claim 13, furtherincluding enabling analysis of said bill schedule.
 17. The programstorage device of claim 13, further including: providing a userinterface for receiving input data from a user and presenting the billschedules.
 18. The program storage device of claim 13, wherein said billschedule includes bill schedule for payment of services, bill schedulefor payment of goods, or combination thereof.
 19. A system forgenerating bill payment schedule, comprising: a memory; a compositepricing model stored in the memory, optimized and generated based on aplurality of pricing models and selected parameters, the compositepricing model operable to determine total price to charge; meansexecutable on a processor, for determining a fraction of each pricingmodel attributing to the composite pricing model, the determining basedon hybrid characteristics of a project wherein the composite pricingmodel includes different pricing models for different sets ofcharacteristics in the project and wherein the fraction can be allocateddifferently in different time units over a time period for payment, thedetermining further based on user input of whether a value for aselected parameter should be used as historical data, triangledistribution data, or normal distribution data and associated value forthe selected parameter, the determining further including automaticallyscreening, the selected parameter for use in a pricing model andrecommending against using the screened parameter based on adetermination made by an automatic process using a significanceestimation for determining significance of the selected parameter,wherein significance estimation is determined using an optimizationmodel,${Max}\frac{\sum\limits_{i = 1}^{n}{x_{i} \times {Mean}\mspace{11mu}( {Parameter}_{i} )}}{\sqrt{\sum\limits_{i = 1}^{n}{x_{i} \times {SD}\mspace{11mu}( {Parameter}_{i} )^{2}}}}$s.t.  x₁, x₂, …  x_(n) ∈ {0, 1} wherein Mean( ) is a function tocalculate the mean of parameter i, SD( ) is a function to calculate astandard deviation of parameter i, x, is a kind of Boolean variable torepresent whether the parameter i is significant or not; means fordetermining target profit margin and risk affordance; means forcomputing a charge fee associated with said each pricing model based onsaid fraction and said total price to charge; means for determining timeperiod for payment, budget and discount rate; means for allocating priceto charge during each time unit of the time period based on said chargefee associated with said each pricing model, said budget and saiddiscount rate; and means for generating a bill schedule based on saidprice to charge during each time unit.